Scholarlink

<p>The classification of colorectal disease based on colonoscopy images requires not only high predictive accuracy but also interpretable decision support. This study proposes a five-stage explainable framework for multi-class colorectal image classification on the Kvasir dataset. This framework is named ColoXAI-RecomNet. The initial stage of this framework involved an investigation of three parallel hybrid CNN ensemble models: RDV-2025 (ResNet50 + DenseNet121 + VGG16), IEM-2025 (InceptionV3 + EfficientNetB0 + MobileNetV2), and DRE-2025 (DenseNet201 + ResNet101 + EfficientNetB3). These CNN models were used as parallel feature extractors. Their combined deep features were used for classification. Among these models, DRE-2025 was identified as one of the top performers in Stage 1. This model had an approximate scale of 77.2&#xa0;M for its backbones. The enhanced features for decision were passed to a multi-class SVM for final classification in Stage 3. The CNN features were refined by an XGBoost model in Stage 2. The final output of this framework was converted to a clinically interpretable recommender output in Stage 5. LIME was incorporated into this framework in Stage 4 to provide visual explanations for each image. The DRE-2025 + XGBoost + SVM model, which performed best, achieved an accuracy of 98.60%, a precision of 98.75%, a recall of 98.50%, an F1-score of 98.62%, and an AUC of 99.30%. In Stage 2, the combined CNN features were improved using an XGBoost algorithm. Stage 3 then classified the Stage 2 outputs using a multi-class SVM with an RBF kernel. Stage 4 used LIME for visual explanation, and Stage 5 converted the final prediction to a clinically interpretable recommender output.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

ColoXAI-RecomNet: Explainable Recommender Framework for Colorectal Cancer Classification Using Integrated CNN Ensemble and LIME Interpretability

  • 期刊论文

  • 发表日期：

    2026年5月26日

查看全文
  • Akella S. Narasimha Raju,
  • Ranjith Kumar Gatla,
  • G. Sucharitha,
  • M. Rajababu,
  • Shaik Jakeer Hussain,
  • Chaganti B. N. Lakshmi,
  • K. Venkatesh,
  • Salem Alqahtani,
  • Mohamed Ghouse,
  • Kasim Sakran Abass

摘要

The classification of colorectal disease based on colonoscopy images requires not only high predictive accuracy but also interpretable decision support. This study proposes a five-stage explainable framework for multi-class colorectal image classification on the Kvasir dataset. This framework is named ColoXAI-RecomNet. The initial stage of this framework involved an investigation of three parallel hybrid CNN ensemble models: RDV-2025 (ResNet50 + DenseNet121 + VGG16), IEM-2025 (InceptionV3 + EfficientNetB0 + MobileNetV2), and DRE-2025 (DenseNet201 + ResNet101 + EfficientNetB3). These CNN models were used as parallel feature extractors. Their combined deep features were used for classification. Among these models, DRE-2025 was identified as one of the top performers in Stage 1. This model had an approximate scale of 77.2 M for its backbones. The enhanced features for decision were passed to a multi-class SVM for final classification in Stage 3. The CNN features were refined by an XGBoost model in Stage 2. The final output of this framework was converted to a clinically interpretable recommender output in Stage 5. LIME was incorporated into this framework in Stage 4 to provide visual explanations for each image. The DRE-2025 + XGBoost + SVM model, which performed best, achieved an accuracy of 98.60%, a precision of 98.75%, a recall of 98.50%, an F1-score of 98.62%, and an AUC of 99.30%. In Stage 2, the combined CNN features were improved using an XGBoost algorithm. Stage 3 then classified the Stage 2 outputs using a multi-class SVM with an RBF kernel. Stage 4 used LIME for visual explanation, and Stage 5 converted the final prediction to a clinically interpretable recommender output.